{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24eb7116",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "088b53f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Figure 8.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "522faeec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"ID\"], color = 'white', s = 50, alpha=1,edgecolor='black',zorder=4)\n",
    "plt.plot(df[\"N\"],df[\"ID\"],color='black',linewidth = 1, zorder=2)\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mD\"], color = 'gray', s = 50, alpha=1,edgecolor='gray',zorder=3)\n",
    "plt.plot(df[\"N\"],df[\"mD\"],color='gray',linewidth = 1, zorder=1,linestyle='--')\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ 0, 400000, 800000, 1200000,1600000], labels = ['','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 1600000])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('ID mD.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c9791bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "plt.axhline(0, color='black', linewidth=1, alpha=1, zorder=1)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mD%\"], color = 'gray', s = 50, alpha=1,edgecolor='gray',zorder=3)\n",
    "plt.plot(df[\"N\"],df[\"mD%\"],color='gray',linewidth = 1, zorder=2)\n",
    "\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ -20, -10, 0, 10,20], labels = ['','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([-20, 20])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Difference mD.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "968306df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"IDIN\"], color = 'white', s = 50, alpha=1,edgecolor='black',zorder=8)\n",
    "plt.plot(df[\"N\"],df[\"IDIN\"],color='black',linewidth = 1, zorder=4)\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mDIN\"], color = 'red', s = 50, alpha=1,edgecolor='red',zorder=5)\n",
    "plt.plot(df[\"N\"],df[\"mDIN\"],color='red',linewidth = 1, zorder=1,linestyle='--')\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"IDmN\"], color = 'green', s = 50, alpha=1,edgecolor='green',zorder=6)\n",
    "plt.plot(df[\"N\"],df[\"IDmN\"],color='green',linewidth = 1, zorder=2,linestyle='--')\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mDmN\"], color = 'blue', s = 50, alpha=1,edgecolor='blue',zorder=7)\n",
    "plt.plot(df[\"N\"],df[\"mDmN\"],color='blue',linewidth = 1, zorder=3,linestyle='--')\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ 0, 1000, 2000, 3000,4000,5000,6000], labels = ['','','','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 6000])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('IDIN IDmN mDIN mDmN.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "168805c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "plt.axhline(0, color='black', linewidth=1, alpha=1, zorder=1)\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mDIN%\"], color = 'red', s = 50, alpha=1,edgecolor='red',zorder=5)\n",
    "plt.plot(df[\"N\"],df[\"mDIN%\"],color='red',linewidth = 1, zorder=2,linestyle='solid')\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"IDmN%\"], color = 'green', s = 50, alpha=1,edgecolor='green',zorder=6)\n",
    "plt.plot(df[\"N\"],df[\"IDmN%\"],color='green',linewidth = 1, zorder=3,linestyle='solid')\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"mDmN%\"], color = 'blue', s = 50, alpha=1,edgecolor='blue',zorder=7)\n",
    "plt.plot(df[\"N\"],df[\"mDmN%\"],color='blue',linewidth = 1, zorder=4,linestyle='solid')\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ -20, -10, 0, 10,20], labels = ['','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([-20, 20])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Difference IDIN.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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